%0 Generic %1 wang2019learning %A Wang, Xiaolong %A Jabri, Allan %A Efros, Alexei A. %D 2019 %K arch loss pose semisup tracking video %T Learning Correspondence from the Cycle-Consistency of Time %U http://arxiv.org/abs/1903.07593 %X We introduce a self-supervised method for learning visual correspondence from unlabeled video. The main idea is to use cycle-consistency in time as free supervisory signal for learning visual representations from scratch. At training time, our model learns a feature map representation to be useful for performing cycle-consistent tracking. At test time, we use the acquired representation to find nearest neighbors across space and time. We demonstrate the generalizability of the representation -- without finetuning -- across a range of visual correspondence tasks, including video object segmentation, keypoint tracking, and optical flow. Our approach outperforms previous self-supervised methods and performs competitively with strongly supervised methods.

@misc{wang2019learning, abstract = {We introduce a self-supervised method for learning visual correspondence from unlabeled video. The main idea is to use cycle-consistency in time as free supervisory signal for learning visual representations from scratch. At training time, our model learns a feature map representation to be useful for performing cycle-consistent tracking. At test time, we use the acquired representation to find nearest neighbors across space and time. We demonstrate the generalizability of the representation -- without finetuning -- across a range of visual correspondence tasks, including video object segmentation, keypoint tracking, and optical flow. Our approach outperforms previous self-supervised methods and performs competitively with strongly supervised methods.}, added-at = {2019-04-09T23:02:43.000+0200}, author = {Wang, Xiaolong and Jabri, Allan and Efros, Alexei A.}, biburl = {https://www.bibsonomy.org/bibtex/2bb7181faee81822bb54d537929acd827/nmatsuk}, description = {Learning Correspondence from the Cycle-Consistency of Time}, interhash = {a4d543ad46a6e11b3cf34c4e9d689020}, intrahash = {bb7181faee81822bb54d537929acd827}, keywords = {arch loss pose semisup tracking video}, note = {cite arxiv:1903.07593Comment: CVPR 2019 Oral. Project page: http://ajabri.github.io/timecycle}, timestamp = {2019-04-09T23:02:43.000+0200}, title = {Learning Correspondence from the Cycle-Consistency of Time}, url = {http://arxiv.org/abs/1903.07593}, year = 2019 }